AIinretailfortheagenticera:Aspirationtoaction

September2025

AIinretailfortheagenticera:Aspirationtoaction

Tableofcontents

Foreword04

Introduction05

Industrylandscape05

KeythemesdrivingAIadoptionintheretailspace05

EmergingtechsupplycatalystsenablingAIadoption06

EvolutionofAIinretail07

Industry-leadingillustrations10

AI-ledinnovationsshapingtheretailindustry11

KeyprinciplesofsuccessfulAIimplementation12

Approachandexecutionframework13

Wayforwardandstrategicimplications15

Connectwithus16

03

AIinretailfortheagenticera:Aspirationtoaction

Foreword

AsIndia’sretailsectorapproachesUS$2trillionby2030,the

roleoftechnology,particularlyartificialintelligence(AI),isnolongerperipheral.Technologyhasafoundationalrolenow.ForChiefExperienceOfficersandtechnologyleaders,thismomentpresentsastrategicinflexionpoint:theshiftfromdigital

enablementtoagentictransformation.

Thisreportoffersaforward-lookinglensintohowAIis

redefiningtherulesofengagement,operationsandinnovationacrosstheretailvaluechain.Fromintelligentagentsthat

autonomouslymanagecustomerjourneystoAI-powered

platformsthataccelerateproductdesignandsupplychainresponsiveness,theusecasesoutlinedhereareaspirationalyetactionable.

Forbusinessleaders,theimperativeisclear:AImustbe

embeddedintothecoreofenterprisestrategy.Thismeans

investinginscalabledatainfrastructure,nurturingcross-

functionalcollaborationandadoptingatest-and-learnmindsettodrivecontinuousinnovation.Fortechnologyleaders,the

challengeliesinarchitectingresilient,cloud-nativeecosystemsthatsupportreal-timeintelligence,whileensuringresponsibleAIpracticesthatupholdtransparency,fairnessandprivacy.

Thisreportofferspracticalinsightstohelpleadershipteamsmovebeyondpilotsandexperiments,towardsscalable

executionandrealimpact.WhetheryouareaChiefExecutiveOfficershapinglong-termstrategy,aChiefTechnologyOfficerbuildingplatformcapabilitiesoraChiefDigitalOfficerleading

omnichanneltransformation;thisreportservesasastrategicguidefornavigatingtheevolvingretaillandscape

AIisacatalystforreimaginingretailandthetimetoleadthere-imaginationisnow!

AnandRamanathan

PartnerandConsumerIndustryLeaderDeloitteIndia

PraveenGovindu

Partner

DeloitteIndia

MoumitaSarker

Partner

DeloitteIndia

04

AIinretailfortheagenticera:Aspirationtoaction

05

Introduction

India’sretailsectorisoneofthemostdynamicintheworld.Itiscurrentlyvaluedat≈US$1trillionandcontributestoover10percentofthecountry,sGrossDomesticProduct(GDP),whileemployingnearly8percentoftheworkforce.1Thissectoris

projectedtoalmostdoubletoUS$1.9trillionby2030,2drivenbyrisingincomes,urbanisationandevolvingconsumer

preferences.Therapidtransformationispoweredbyrobustdomesticconsumptionalongsideasurgeindigitaladoption,premiumisationandtherapidriseofe-commerceacrossbothurbanandemergingmarkets.

Between2020and2024,India’sretailande-commerce

sectorshavesignificantlyincreasedinvestmentsinartificial

intelligence(AI)toenhancecustomerexperiences,optimise

operationsanddrivesalesgrowth.Theretailsectorisenteringatransformativeeradefinedbytechnology-driven,sustainableandhyper-personalisedconsumerexperiences.

Industrylandscape

AI-driventransformation,evolvingconsumerhabitsand

structuralshiftsareunlockingnewgrowthavenuesforIndia’sretailsector.Demographics,technologyandpolicychangesarereshapinghowandwhereconsumersshop,drivingthenext

phaseofretailevolution.

KeythemesdrivingAIadoptionintheretailspace

Riseofthechannel-agnosticshopper

Consumerbehaviourisshiftingawayfromsingle-channel

interactions.Today’sconsumerischannel-agnostic,expectingseamlessexperiencesandhandoffsacrossofflineandonlinetouchpoints.Inresponse,retailersareincreasinglyturningtoAI-poweredsolutionstointelligentlyintegratejourneys,hyper-personaliseengagementandoptimiseoperationsacrossthefullspectrumofchannels.

Digitalisationande-commerceboom

E-commerceisprojectedtoreachINR27trillionby2030,3

drivenbydigitalaccess,easeofpaymentsandimmersive

experiences.Quickcommerceandsocialcommercearegaining

Indianretailmarketbychannel(inUS$billion)

~1,930

~1,010

8%

80%

12%

17%

12%

71%

20232030

GeneralTradeModerntradeE-commerceSource:FICCIMassmerize2025report

traction,especiallyinsmallercities.Consumersnowexpect

convenience,speedandpersonalisationacrossplatforms.4AIisplayingakeyrolebyhelpingbrandspredictpreferences,

personaliseoffersandautomatecustomerservicetomakeshoppingsmarterandfaster

Urbanisationandchangingfamilydynamics

India’surbanpopulationisexpectedtoreach40percentby2030,5withoverhalfofthehouseholdsbeingnuclear.Thisshiftisexpandingthebaseoffirst-timeusersofbrandedandconvenience-ledproducts.Retailersmustadapttoevolvingurbanlifestylesandconsumptionpatterns.6

Growthofomnichannelretailing

Retailersaremovingtowardsintegratedomnichannelmodelstomeetevolvingconsumerexpectations.Shoppersdemandseamlesstransitionsacrossonline,appandin-storejourneys.Thisshiftenhancescustomersatisfaction,loyaltyand

repeatpurchases.7AIisenablingretailerstotrackcustomerbehaviouracrosschannels,personaliseinteractionsinrealtimeandoptimiseinventoryanddeliverysystems.

1

/news/industry/india-set-to-become-3rd-largest-economy-by-2030-driven-by-demographic-dividend

-report/99460554

2

/news/economy/indicators/india-poised-to-become-third-largest-consumer-market-wef/articleshow/67450935.cms

3Deloittereport

4FICCImasmerizereport

5

.in/PressReleaseIframePage.aspx?PRID=2042542

6FICCImassmerizereport

7FICCImassmerizereport

AIinretailfortheagenticera:Aspirationtoaction

06

Growingdemandforcustomisedandsustainableproducts

Consumersincreasinglyseekpersonalised,region-specificandsustainableofferings.ThisisdrivingStockKeepingUnit(SKU)proliferationandtheneedforefficientinventorymanagement.MillennialsandGenZareleadingdemandforvalue-aligned,

expressiveandlimited-editionproducts.8

Premiumisationleadinggrowth

Risingincomesandglobalexposureareaccelerating

premiumisationacrossconsumersegments.Super-rich

householdsareexpectedtogrow5xby2030,withrising

demandevenintier2–4cities.Onlineplatformsareenablingaccesstopremiumandglobal-qualityproducts.9

EmergingtechnologiesinAI

RetailersareadoptingAIandanalyticstoenhancecustomer

experienceandoperationalefficiencyacrossretailvaluechain.Technologiessuchasvirtualtrials,self-checkoutsandsmart

inventorysystemsarebecomingmainstream.10

Increasingspendingpowerofcustomers

Themiddle-incomesegmentisgrowingrapidly,fuelling

discretionaryandaspirationalspendingonfashion,electronicsandbeauty.GenZ,withprojectedspendingofUS$250Bby

2025,11isreshapingconsumptiontrends.Theirdigitalfluencyandevolvingpreferencesdemandagilebrandstrategies.12

EmergingtechsupplycatalystsenablingAIadoption

AdvancesinLargeLanguageModels(LLMs)13

BreakthroughsinLLMshavegivenAImuchdeeper

contextualunderstandingandlanguagefluency,enabling

morehuman-likeinteractions.RetailerscandeployadvancedgenerativeAI(GenAI)chatbotswhoseconversational

abilitiesmakethemeffectivesmart-shoppingassistantsandcustomerserviceagents.Withsupportfor18+languages,

thesemodelscanengagediversecustomerbasesseamlessly.Byofferingmorenatural,personaliseddialogueswith

customers;theseimprovedmodelsareacceleratingAIuseinsalesandsupport.

Decliningcomputecostsandcloud-nativeAIservices

Reducedcomputingcostsandtheubiquityofcloud-nativeAIservicesareloweringbarrierstoadoptingscalableAI.

ThepriceofusingadvancedAImodelshasreduced,for

example,somegenerativelanguagemodelApplication

programminginterface(API)costshavedropped

significantlyinthepastyear,makingexperimentationfarmoreaffordable.Meanwhile,majorcloudprovidersnowofferscalable,pay-as-you-goAIplatforms,allowingevenmid-tierretailerstoimplementAIsolutionswithoutheavyupfrontinfrastructureinvestments.14

8FICCImassmerizereport

9FICCImassmerizereport

10FICCImassmerizereport

11

/gen-zs-collective-spending-power-reaches-860-billion-snap-inc-and-bcgs-india-first-report/

12FICCImassmerizereport

13TheLatestAdvancementsinLargeLanguageModels:Cap,Medium,July2025

14Indiaofferscomputeatone-fifthofglobalpricesforAI,MoneyControl

AIinretailfortheagenticera:Aspirationtoaction

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Improveddatainfrastructureandintegration

Companieshaveinvestedinrobustdatainfrastructurethat

enablesAIatscale.Moderndataplatformsandintegration

toolsbreakdownsilostoprovideasingle,high-qualityview

ofcustomersandinventoryacrosschannels.Thisstrongdatafoundation,withenterprisedatalakes,real-timedatapipelinesandbetterdatagovernance,ensuresAImodelscanbefedrich,unifieddatasets;improvingtheiraccuracyandimpactinretailusecases.15

Open-sourceAItoolsandpre-trainedmodels

Theopen-sourceAIecosystemhasmadecutting-edge

modelsandtoolswidelyaccessible.Pre-trainedmodels

haverapidlyimprovedandisnowclosingtheperformance

gapwithproprietaryAIsystems.Businessesareembracingtheseopensolutionsfortheirflexibilityandlowercosts.Withhigh-performingmodelsandlibrariesavailableforfreeoratlowcost,evensmallerretailerscanimplementadvancedAIcapabilities,democratisinginnovationacrosstheindustry.16

Growthofedgecomputingandreal-timeprocessing

Retailersarelikelytoincreasetheuseofedgecomputing

torunAIalgorithmsinrealtimeatstoresanddistributioncentres.Processingdatalocally(forexample,adigitalmenu

carddynamicallypresentinghyper-personaliseddiscountsandproductofferingstailoredtoeachconsumerpersona,analysingvideofromin-storecamerasforautomated

checkoutorshelfanalytics)minimiseslatencyandcloud

bandwidthuse,enablinginstantinsightsontheshopfloor.

CompanieshavealreadyadoptedAI-poweredcomputer

visionsystemscombiningcameraswithedgeAIprocessing.

Suchedgeinfrastructuremakeslarge-scale,real-timeretailAIdeploymentspracticalandresilient,fromsmartstorestosupplychainoptimisations.17

AI-focusedhardwareinnovations

SpecialisedAIhardware,fromadvancedGraphicsProcessingUnits(GPUs)tocustomAIaccelerators,isdramatically

boostingtheperformanceandefficiencyofAIworkloads.

Ongoingchipinnovationsmeanmodelscanbetrainedtorunfasterandmorecost-effectivelythanever.Forexample,newpurpose-builtchipswilldeliverhigherthroughputwithlowercostperinferencecomparedwithprior-generationhardware.Inretail,thistranslatesintocapabilitiessuchasreal-time

computervisionforautomatedcheckoutsystemsorhigh-speedrecommendationenginesthatpersonaliseoffers

instantlyduringonlineshoppingsessions.ThesehardwaregainsaremakingcomputationallyintensiveAIapplicationseconomicallyviableatscaleforretailers,furtherpropellingAIadoption.

EvolutionofAIinretail

HowAladvancementsareenablingtheshiftsinretailindustry

Drivenby…

?Alinretailhasevolvedfromstatistical

analysisforcampaignsandproductlaunchestoadvancedmachinelearningforchurn

prediction,customersegmentation,anddemandforecasting.

?Deeplearningapplicationsnowsupportvisualrecognitionandvirtualtry-ons,enhancingcustomerinteraction.

?TheemergenceofGenerativeAland

AgenticAlisdrivingashifttowardshyper-personalizedcontent,productdesign,andautomated,agent-ledprocessmanagement.

?Theseadvancementsimprovecustomer

experience,operationalefficiency,and

enabledata-drivendecision-makingacrossretailfunctions.

Statistics

Descriptiveanddiagnostic

Machinelearning

Predictiveandprescriptive

Deeplearning

Computervision

GenerativeAI

Hyperpersonalisation,Contentcreation

AgenticAI

Automatedprocessmanagement

15Low-costIndiaseenaspotentialregionalhubindatacentreboom,FinancialTimes

16TheGapBetweenOpenandClosedAIModelsMightBeShrinking.Here’sWhyThatMatters,Time

17TheRiseofEdgeComputinginRetail:TransformingStoreOperationsandCustomerExperience,Medium

AIinretailfortheagenticera:Aspirationtoaction

08

AIadoptioninretailhasevolvedfrombasicanalyticsto

advancedmachinelearninganddeeplearningapplications,

enhancingbothcustomerengagementandoperational

efficiency.TheemergenceofGenAIandAgenticAIisnow

drivinginnovativeproductdesignandautonomousprocess

management,thustransformingtheretailvaluechain.IndiasAIspendingisalsosettotriple,risingfromUS$78billionin

FY23toUS$2022billionbyFY27E,growingat3035percentannually.Thissurgereflectsthecountry’srisingadoptionofAIacrosssectors.

WhyshouldretailcompaniesinvestinAI18,19,20

AIisemergingasacriticalenablerforvaluerealisationinretail.ByinvestinginAI,companiescanunlockmeasurableimprovementsacrossthreedimensions:Efficiency,

ExperienceandIntelligence.

India'sspendingonAI,2023–27(US$billion)

20–22

+30–35%

7–8

FY23FY27E

Source:AttractingAIDataCentreInfrastructureInvestmentinIndia

Efficiency–Doingmorewithless

2

3

4

5

6

7

8

9

1Automateprocessestoreducemanualinterventionandstreamlineworkflows

Optimisecostsacrossoperations,logisticsandback-endfunctions

Driveconsistencybystandardisingoutcomesandreducingvariability

ReduceFull-TimeEquivalents(FTEs)throughautomation,enablingredeploymentofstafftohigher-valuetasks

Removewastebyimprovingresourceutilisationandminimisinginefficiencies

Improvespeedincoreprocessessuchassupplychain,merchandisingandreporting

Profitabilityandmarginimprovementbyloweringselling,generalandadministrativeexpensesandcostofgoodssoldaspercentageofrevenue

Qualityimprovementbyreducingerrorscausedbyhumanintervention

Leadtimereduction,e.g.fastermonth-endfinancialreporting

18DeloitteAnalysis

19VoiceAImovesbeyondscriptsasIndianfirmstapmultilingualbots,EconomicTimes

20TheAIAdoptionRealityCheck:FirmswithAIStrategiesareTwiceasLikelytoseeAI-drivenRevenueGrowth;ThoseWithoutRiskFallingBehind,ThomsonReuters

AIinretailfortheagenticera:Aspirationtoaction

09

Experience–Creatingfit-for-purposeinteractions

Personalisecontenttodelivertargetedoffersandrecommendations

1

Enhancequalityandoutcomesofcustomerinteractions,ensuringsatisfaction

2

Amplifycreativityinmarketingcampaignsandproductinnovation

3

Simplifyinteractionsacrossdigitalandphysicaltouchpointsforseamlessjourneys

4

Differentiateservicestostandoutincompetitivemarkets

5

Consumerandchannelcentricity,supportedbyreal-timecustomersupport

6

EmployeeengagementthroughAI-botsprovidingquickqueryresolution

7

Newdigitalproductsandservices,suchasdynamicadpricingtailoredtotimeslots

8

Intelligence–Strengtheningdata-drivendecisionmaking

Generatenewinsightsbyminingenterpriseandconsumerdata

1

Improveadaptabilitybyrespondingswiftlytoshiftingmarketconditions

2

Improvedecision-makingwithpredictiveandprescriptiveanalytics

3

AugmentworkforceskillsbyequippingemployeeswithAI-driventools

4

Future-prooftechnologiesbuiltonpivotal,fit-for-purposearchitectures

5

Businessmodelagilitywithfastertime-to-marketfornewofferings

6

Work,workforceandworkplaceofthefuture,leveragingvirtual,automatedandaugmentedworkforces.

7

AIinretailfortheagenticera:Aspirationtoaction

10

Industry-leadingillustrations

ThewidespreaduseofAIisrapidlyreshapingIndia’sretailsectorenablingbusinessestodeliverpersonalisedconsumer

experiences,optimiseoperationsandscalewithagility.LeadingplayersacrosscategoriesareapplyingAIindifferentiatedwaystostrengthencompetitivenessandresilience.BelowareafewcasestudiesthatillustratehowIndia’sretailecosystemismovingbeyondpilot-stageexperimentationtoscaledAI-driventransformation.

LeadingAIstartup

?BuildingLLMsforIndianlanguages,enablinginclusive,localisedconsumerengagement21

?Partneringwithretailerstolaunchvoice-basedAIassistants,improvingaccessibilityandcustomerserviceacrossdiversedemographics

Leadingretailconglomerate

?DeployingAI-poweredcustomeranalyticstopersonaliseoffersandoptimiseassortmentacrossitsmulti-formatstorenetwork22

?UsingAI-drivensupplychainoptimisationtoimproveinventoryturnoverandstreamlinedistributionacrosschannels

Leadingglobale-commercemarketplace

?LaunchedaGenAI-poweredconversationalassistantwithinitsapptoenhancetheonlineshoppingjourney

?Theassistantoffersreal-timeanswers,productsuggestionsandinsights,suchasreviewsandtrends,tohelpcustomersmakeinformeddecisions23

Leadingonlinefooddeliveryaggregator

?UsingAIalgorithmstodeliverpersonalisedmealrecommendations,analysingpastorders,preferencesandlocationtoenhancecustomerexperienceandboostengagement24

?Adedicatedin-appAIchatbotforpremiumsubscribersassistswithfoodandbeveragequeries,offeringtailoredsuggestionsandhelpingusersdecidetheirnextorder

21NetscribesArtificialIntelligenceinRetail&E-commerceReport

22NetscribesArtificialIntelligenceinRetail&E-commerceReport

23NetscribesArtificialIntelligenceinRetail&E-commerceReport

24NetscribesArtificialIntelligenceinRetail&E-commerceReport

AIinretailfortheagenticera:Aspirationtoaction

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AI-ledinnovationshapingtheretailindustry

Usecasesacrossthevaluechain

BrandandmarketingProductdesignOperationalexcellence

AIshoppingconcierge

Postpurchaseservice

agent

Marketingcontentgeneration

Consumerinsightsgeneration

VOCsentiment&themeidentification

Marketingmixmodel

Recommendation

engine

Churnprediction&feedbackanalysis

Designvalidationagent

Rapidprototyping

agent

Productdesign/Image

generator

Retailoperations

Salesforceagent

Staffingadvisoragent

Automatedstafftraining

Storelayoutsimulation

Salesscriptgeneration

Storeperformanceanalytics

Smartstore

Locationintelligencefornextstore

AgenticAIuse-cases

Inventoryassortment

Forecasting

Productpricing

AIuse-cases

Merchandising

Assortmentstrategy

agent

Allocationautomation

agent

Qualitycheckagent

Competitorintelligencesummarisation

Knowledgemanagement

SOPgenerator

Newproductdevelopment

Featureanalytics

SKUrationalisation

GenAIuse-cases

Workflowautomation

P2Pprocessautomation

Efficiencymonitoring

Trainingmaterial

generator

FAQautomation

Investorsummarypackgenerator

HR–Employee

retentionprediction

Frauddetection

Routeplanningandoptimisation

AI-driveninnovation,fueledbyreal-timecontentgenerationthroughGenAI,theemergenceofintelligentagentsand

acceleratedresponsetimes,hascatalysedashiftintheretailindustry.Theseadvancementshavesignificantlyenhancedtheomnichannelcustomerexperience,enabledacceleratedproductturnoverandfacilitatedautomatedissueresolutionthroughagent-ledprocesses,collectivelydrivingrevenue

growthandoperationalefficiency.

1.Hyper-personalisedmarketingcontentgenerationformicro-segments

GenAIhassignificantlyacceleratedandstreamlinedthecreationofhyper-personalisedmarketingcontent.

Thisadvancementenablesmarketerstoengagemicro-segmentswithmoreprecise,tailoredandimpactful

communications.AsindustryconfidenceinGenAIgrows,itsadoptioncontributestohighercampaignconversionrates,ultimatelydrivingsales.

2.Aidedpurchases–ChatbottoAIagentjourney

AIagentshavebolsteredautomation,markinga

progressionfrombasicchatbotstoAI-drivenagents.Whiletraditionalchatbotsprimarilyrespondtoqueriesand

sharedinformation,modernAIagentsactivelysupporttheentirepurchasejourney,ensuringaseamlessanduninterruptedexperience.

Theseagentsengagewithcustomersthrough

intelligentinteraction,offeringpersonalisedproduct

recommendations,targetedpromotionsandfacilitatingtransactioncompletions.Thisevolutionhasmadethe

omnichannelexperiencemoreintegratedandresponsive,directlycontributingtoincreasedsalesandcustomer

satisfaction.

AIinretailfortheagenticera:Aspirationtoaction

12

3.ImprovingcustomerexperiencethroughenhancedresponsespeedenabledbyAIagents

TheadoptionofAIagentshassignificantlyimproved

responsetimesforcustomerfeedbackanddispute

resolution.Usingenterprise-wideknowledgesearch,

theseagentsprovideaone-stopsolutionforswiftlyandaccuratelyaddressingcustomerconcerns.

AIagentsarecapableofindependentlyengagingwith

customers,understandingqueries,identifyingappropriateresolutions,executingnecessaryactionsandclosingthe

loop,minimisingtheneedforhumaninterventionandreducingresponsedelays.

4.Acceleratingfasterproductturn-around

Consumerstodayexhibitlowtoleranceforextended

waitingtimesfornewproducts,drivenbytheriseofquickcommerce.Inresponse,retailersareusingAItoaccelerateproductturnovercyclesthroughatwo-stepapproach:

identifyingemergingmarkettrendsandexpeditingsupplytostores.

Forinstance,fashionretailersutiliseAI-poweredinsightsandtrend-spottingtoolsthatanalysedatafromfashionblogs,socialmedia,magazinesandcustomerfeedback.

Theseinsightssupportdesignersinshorteningdesign

cyclesandrunningsimulations.Concurrently,integrated

supplychainsemploymachinelearning-basedoptimisationalgorithmsandadvanceddemandforecastingtoensure

fasterinventoryreplenishment.

5.Next-genAI-poweredbusinessinsights

Sourcesincludinginternalsales,customerfeedback,competitiveintelligenceandweb-basedinformation

intoaunifiedplatform.ByusingGenAIandintelligent

agents,itdeliversproactive,actionableinsightsatbusinessleaders’fingertipsandoffersaninteractivelayerfor

deeperanalysis.

Byacceleratingtheinsight-to-actioncycle,thisplatform

empowersbusinessleaderstomakefaster,moreinformeddecisions,usheringinaneweraofdata-drivenbusiness

intelligence.

6.AI-Drivendeadstockliquidationagent

AnautonomousAIagentmonitorsSKUvelocityand

inventoryageinginrealtime,triggeringproactive

liquidationstrategiessuchasmicro-bundling,segmenteddiscountingandchannelre-routing.Itcontinuouslylearnsfrompastoutcomestooptimisefutureactions.Thisinturnacceleratesliquidationcycles,improvesmarginrecovery,reducesinventory-relatedworkingcapitalandsupports

Environment,SustainabilityandGovernance(ESG)goalsbyminimisingwaste.

7.DigitalTwinformerchandisingassortmentsimulation

AI-poweredDigitalTwinssimulateeachstore’slayout,

capacity,demographicsandSKUhistory,enabling

merchandiserstotestassortmentchanges.Reinforcementlearningrecommendsoptimal,store-specificmixesto

maximiseGrossMarginReturnonInvestment(GMROI)andsell-through.

Thisimprovessell-through,reducesend-seasonredistributioncosts,boostsGMROIandenhancescustomersatisfactionwithlocalisedassortments.

KeyprinciplesforsuccessfulAIimplementation

AIisgainingtractionacrosssectors,butchallengessuchasinfrastructuregaps,talentshortagesandinternalresistancestillhinderfull-scaleadoption.

BarrierstodevelopinganddeployingGenAI

+10pts+6pts-10pts

38%

28%

32%

36%

26%

22%25%

21%20%17%17%17%15%15%19%14%

26%27%26%27%24%

18%

Worries

about

complying

with

regulations

Difficulty

managing

risks

Implementation

challenges

Lackof

technical

talentand

skills

Lackofa

governance

model

Difficulty

identifying

usecases

Lackofan

adoption

strategy

Trouble

choosingthe

right

technologies

Cultural

resistance

from

employees

Not

havingthe

rightcomp

infrastructure/

data

Lackof

executive

commitment

and/or

funding

Q1Q4

Source:StateofGenAIreport

AIinretailfortheagenticera:Aspirationtoaction

13

Whenitcomestotheretailindustry,thespecificchallengesthattheyfaceinAIimplementationsare:

1.Dataqualityandintegration:AIsystemsrelyheavily

onclean,consistentandwell-integrateddata.Inretail,

dataoftencomesfrommultiplesources,suchasPoint

ofSale(POS)systems,e-commerceplatforms,CustomerRelationshipManagement(CRM)tools,supplychain

databasesandsocialmedia.Thesesystemsmaynotspeakthesamelanguage,leadingtofragmentedinsights.25

2.Dataprivacyandsecurity:Asretailerscollectvast

amountsofcustomerdatatoenhancepersonalisation,

theyfaceincreasingscrutinyoverhowthatdataisstored,processedandprotected.WithIndia’sevolvingdata

protectionregulations(e.g.DigitalPersonalDataProtectionAct),complianceisbecomingmorecomplex.26

3.Highimplementationcosts:AIadoptionrequiressignificantinvestmentininfrastructure(cloudcomputing,datalakes),

so